Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...

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Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...

Have you ever wished you could generate interactive websites with HTML, CSS, and JavaScript while programming in nothing but Python? Here are three frameworks that do the trick. Python has long had a ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

In artificial intelligence, one common challenge is ensuring that language models can process information quickly and efficiently. Imagine you’re trying to use a language model to generate text or ...

I'm using llama-cpp-python==0.2.60, installed using this command CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python. I'm able to load a model using type_k=8 and type_v=8 (for q8_0 cache).

HuggingFace Researchers introduce Quanto to address the challenge of optimizing deep learning models for deployment on resource-constrained devices, such as mobile phones and embedded systems. Instead ...